This study presents the results of a quasi-experiment (N= 307) conducted over the course of 10 weeks in Spring of 2016 to assess the effectiveness of a game platform designed to facilitate political engagement, attention, efficacy, knowledge, and participation among college students. Results indicate positive effects of gameplay on several key dimensions of political engagement, including voter registration, virtual political participation (following a candidate on Twitter, liking a candidate on Facebook, and watching debates), and consumption of public affairs information (including National Public Radio, non-NPR political talk radio, and online news aggregator sites). Additionally, gameplay provided significantly greater benefits to students with the lowest rates of political knowledge at baseline. Overall, participants reported high rates of game satisfaction, with 79% of participants reporting being very to somewhat pleased if they were asked to play the game again. These results are discussed in terms of the implications for civics education, pedagogy, and political engagement among young people.

Social and medical scientists are often concerned that the external validity of experimental results may be compromised because of heterogeneous treatment effects. If a treatment has different effects on those who would choose to take it and those who would not, the average treatment effect estimated in a standard randomized controlled trial (RCT) may give a misleading picture of its impact outside of the study sample. Patient preference trials (PPTs), where participants’ preferences over treatment options are incorporated in the study design, provide a possible solution. In this paper, we provide a systematic analysis of PPTs based on the potential outcomes framework of causal inference. We propose a general design for PPTs with multi-valued treatments, where participants state their pre- ferred treatments and are then randomized into either a standard RCT or a self-selection condition. We derive nonparametric sharp bounds on the average causal effects among each choice-based sub- population of participants under the proposed design. We also propose a sensitivity analysis for the violation of the key ignorability assumption sufficient for identifying the target causal quantity. The proposed design and methodology are illustrated with an original study of partisan news media and its behavioral impact.

Democratic publics have always struggled to constrain their elected leaders’ foreign policy actions. By its nature, foreign policy creates information asymmetries that disadvantage citizens in favor of leaders. But has this disadvantage deepened with the advent of the Internet and the resulting fundamental changes in the media and politics? We argue that it has. The current information and political environments erode constraint by inclining constituents to reflexively and durably back “their” leaders and disapprove of opposition. These changes make it harder for citizens to informationally “catch up” with and constrain leaders because views that contradict citizens’ beliefs are less likely to break through when media are fragmented and siloed. These changes have important implications for theories concerning the democratic peace, audience costs, rally effects, and diversionary war. They may also contribute to instability in foreign policy by contributing to sudden and destabilizing changes in public opinion that undercut commitments abroad.

Using both survey- and platform-based measures of support, we study how polarization manifests for 4,313 of President Donald Trump’s tweets since he was inaugurated in 2017. We find high levels of polarization in response to Trump’s tweets. However, after controlling for mean differences, we surprisingly find a high degree of agreement across partisan lines across both survey and platform-based measures. This suggests that Republicans and Democrats, while disagreeing on an absolute level, tend to agree on the relative quality of Trump’s tweets. We assess potential reasons for this, for example, by studying how support changes in response to tweets containing positive versus negative language.We also explore how Democrats and Republicans respond to tweets containing insults of individuals with particular socio-demographics, finding that Republican support decreases when Republicans, relative to Democrats, are insulted, and Democrats respond negatively to insults of women and members of the media.